Multi-omics analyses cannot identify true-positive novel associations from underpowered genome-wide association studies of four brain-related traits

BackgroundThe integration of multi-omics information (e.g., epigenetics and transcriptomics) can be useful for interpreting findings from genome-wide association studies (GWAS). It has additionally been suggested that multi-omics may aid in novel variant discovery, thus circumventing the need to increase GWAS sample sizes. We tested whether incorporating multi-omics information in earlier and smaller sized GWAS boosts true-positive discovery of genes that were later revealed by larger GWAS of the same/similar traits. MethodsWe applied ten different analytic approaches to integrating multi-omics data from twelve sources (e.g., Genotype-Tissue Expression project) to test whether earlier and smaller GWAS of 4 brain-related traits (i.e., alcohol use disorder/problematic alcohol use [AUD/PAU], major depression [MDD], schizophrenia [SCZ], and intracranial volume [ICV]) could detect genes that were revealed by a later and larger GWAS. ResultsMulti-omics data did not reliably identify novel genes in earlier less powered GWAS (PPV<0.2; 80% false-positive associations). Machine learning predictions marginally increased the number of identified novel genes, correctly identifying 1-8 additional genes, but only for well-powered early GWAS of highly heritable traits (i.e., ICV and SCZ). Multi-omics, particularly positional mapping (i.e., fastBAT, MAGMA, and H-MAGMA), was useful for prioritizing genes within genome-wide significant loci (PPVs = 0.5 - 1.0). ConclusionsAlthough the integration of multi-omics information, particularly when multiple methods agree, helps prioritize GWAS findings and translate them into information about disease biology, it does not substantively increase novel gene discovery in brain-related GWAS. To increase power for discovery of novel genes and loci, increasing sample size is a requirement.